Capabilities and Reputation Risks Towards Firm Performance
Bibliographic record
Abstract
The effects of firm-specific resources on firm performance has been a quest of many and widely studied worldwide. In today’s business environment, arguments suggesting the relative importance of firm-specific resources in explaining firm performance variation are said to be of the greatest influence on the study of firm behavior. On the other hand, firms with strong, positive reputations can attract and retain crucial talent and often have loyal customers likely to buy a broader range of products and services. It can lead to higher sales generated by satisfied customers and their referrals and can potentially raise capital and share price, and improve the firm performance. An empirical study such as this attempts to investigate the combinations of resources of the firm and focus on reputational risk management concerning firm performance. As such, this study involves variables partially adopted from Donabedian Theory, such as intangible resources, namely capability as an exogenous construct towards endogenous construct and firm performance, as well as proposing a mediation model to analyze the mediated relationship of reputational risk in accelerating the relationship between capabilities and firm performance. This study applies variance-based structural equation modeling via Smart PLS to a sample of 161 listed firms in Malaysia as respondents. A judgment purposive sampling technique has been adopted as the respondents are derived from listed firms under Malaysian Bourse. Overall, the findings of this study reveal how firms may gain competitive advantages in terms of their reputation and eventually be able to sustain their firm’s performances by implementing an integrative model of intangible resources such as capabilities and in their routines and processes within the firms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".